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Model predictive control (MPC) has been studied in the building science realm for about three decades. However, the following two aspects of the building control have not been studied thoroughly in MPC research. One is the impact of the mixed-mode cooling system on the active heating ventilation and air conditioning (HVAC) energy consumption, and the other is the differences of individual thermal comfort preference and its impact on energy. This paper proposes an occupant-oriented mixed-mode EnergyPlus predictive control system to optimize HVAC energy consumption while meeting the individual thermal comfort preference. A web-based dashboard is implemented in the test-bed building for three months to collect individual thermal comfort preference data. The data analysis results suggest that occupants have various tolerances and preferences about thermal comfort. The simulation results show that, during one week of a typical swing season, the mixed-mode system further reduces the active HVAC energy consumption, and the diversified occupant thermal comfort preference has significant impact on HVAC energy consumption. [ABSTRACT FROM AUTHOR]

Abstract: To streamline the design of the energy efficient buildings, appropriate tools are needed to assess their energy performance taking into account the microclimatic context. Numerical simulation seems to be the most suitable issue, but none tool is dedicate to the direct evaluation of the microclimate influence on the building energy consumption. A complete solution could be to use both CFD and thermoradiative simulation tools complementary with the coupling technique perspective. This paper presents both a developed CFD–thermoradiative coupled simulation tool and a typical application on an urban fragment. The results lead to two kind of observations: [•] Integration of the thermal model of a building in the microclimatic simulation platform enable a quantitative evaluation of the building energy demand regarding different urban design scenarii (e.g. mineralized vs vegetated). [•] Different physical phenomena do not contribute as much in the energy balance and it is important to compute precisely each one to obtain the small scale microclimatic influence. [Copyright &y& Elsevier]

Abstract: Mean wind pressure coefficients (C p) are key input parameters for air infiltration and ventilation studies. However, building energy simulation and stand-alone airflow network programs usually only provide and/or use a limited amount of C p data, which are based on several assumptions. An important assumption consists of using surface-averaged C p values instead of local C p values with a high resolution in space. This paper provides information on the uncertainty in the calculated airflow rate due to the use of surface-averaged C p data. The study is performed using published empirical data on pressure coefficients obtained from extensive wind tunnel experiments. The uncertainty is assessed based on the comparison of the airflow rate (ϕ) calculated using the surface-averaged C p values (ϕ AV) and the airflow rate calculated using local C p values (ϕ LOC). The results indicate that the uncertainty with a confidence interval of 95% is high: 0.23 ϕ AV < ϕ LOC <5.07 ϕ AV. In cases with the largest surface-averaged ΔC p, the underestimation or overestimation is smaller but not negligible: 0.52 ϕ AV < ϕ LOC <1.42 ϕ AV. These results provide boundaries for future improvements in C p data quality, and new developments can be evaluated by comparison with the uncertainty of the current methods. [Copyright &y& Elsevier]

Abstract: The Kyoto protocol binded the developed countries to reduce the greenhouse gas emissions at least by 5% by 2008–2012 in order to tackle global warming and climate change. Some of the measures of the governments to achieve this goal are to promote new buildings construction and to retrofit existing buildings while satisfying low energy criteria. This means improving energy efficiency of buildings and energy systems, developing sustainable building concepts and promoting renewable energy sources. The design of a low energy building requires parametric studies via simulation tools in order to optimize the design of the building envelope and HVAC systems. These studies are often complex and time consuming due to a large number of parameters to consider. Hence, this paper aims to set up a methodology that simplifies parametrical studies during the design process of a low energy building. The methodology is based on the Design of Experiments (DOE) method which is a statistical method widely used in industry to perform parametric studies that reduces the required number of experiments. [Copyright &y& Elsevier]